Assessing tree germination resilience to global warming: a manipulative experiment using sugar maple (<i>Acer saccharum</i>)
Bibliographic record
Abstract
Abstract A climate warming of 2–5°C by the end of the century will impact the likelihood of seed germination of sugar maple (Acer saccharum), a dominant tree species which possesses a restricted temperature range to ensure successful reproduction. We hypothesize that seed origin affects germination due to the species' local adaptation to temperature. We tested this by experimentally investigating the effect of incubation temperature and temperature shifting on sugar maple seed germination from seven different seed sources representing the current species range. Survival analysis showed that seeds from the northern range had the highest germination percentage, while the southern range had the lowest. The mean germination percentage under constant temperatures was best when temperatures were ≤5°C, whereas germination percentages plummeted at temperatures ≥11°C (5.8%). Cool shifting increased germination by 19.1% over constant temperature treatments and by 29.3% over warm shifting treatments. Both shifting treatments caused earlier germination relative to the constant temperature treatments. A climate warming of up to +5°C is shown to severely reduce germination of seeds from the southern range. However, under a more pronounced warming of 7°C, seed germination at the northern range become more affected and now comparable to those found from the southern range. This study states that the high seed germination percentage found in sugar maple at the northern range makes it fairly resilient to the warmest projected temperature increase for the next century. These findings provide forest managers with the necessary information to make accurate projections when considering strategies for future regeneration while also considering climate warming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".